1. Field of the Invention
The present invention relates to a method of sensing melt-front position and velocity, and more particularly to a sensing method capable of predicting state variables of a melt-injection device and further using the predicted values and actually measured values to perform an error correction so as to reliably calculate the melt-front position and velocity.
2. Related Art
Generally, the melt-front sensing mechanisms of a melt-injection device can be categorized into two classes: one being the hardware-based and the other the software-based. The hardware-based sensing approaches can be further divided into two types, the non-contact type and the contact one. One example of the non-contact sensing is the ultrasonic sensing, in which an ultrasonic probe is installed on the outer surface of the mold to emit detection waves and to receive the reflected wave so as to determine the state of the melt. The non-contact feature is the advantage of this approach, but the high cost of the sensing equipment prevents widespread installation of the non-contact sensors. An example of the contact sensing is the capacitive sensing, in which an electrode plate is adhered on the inner wall of the mold cavity, and the melt-front position and velocity are detected by measuring the capacitance variation when the melt flows over the electrode plate. Because of the high pressure and fast flow velocity inside the mold cavity, reliable installation of the sensing electrode becomes the major concern for the capacitive sensing. In fact, the possibly unreliable installation is a general concern with any contact sensing approach.
With the stated problems associated with existing hardware sensing methods, the software-based sensing approach, also called the virtual-sensing for not requiring any hardware sensor, constitutes a competitive alternative. By taking four signals as its inputs, including the displacement and velocity of an injection screw, the nozzle pressure, and the nozzle temperature, a recently proposed software sensing method outputs the melt-front position by employing an artificial neural network which predicts the values of the state variables of the melt-injection device at the next sampling time based on the current values of the state variables and the input signals. This software sensing approach belongs to the so-called open-loop prediction, where the output signal is predicted solely based on the input signals without any feedback correction of the prediction error. Lacking proper error feedback correction, accuracy of the melt front position predicted by the current software sensing method deteriorates when the input signals are interfered by external disturbances or when the melt injection situation is changed.